Lower prevalence of multiple sclerosis in First Nations Canadians
Bibliographic record
Abstract
BACKGROUND: We compared the incidence and prevalence of multiple sclerosis (MS) between First Nations (FN) and non-FN populations in Manitoba. METHODS: We applied previously validated algorithms to population-based administrative (health claims) data from Manitoba, Canada, to identify all persons with MS from 1984 to 2011. We identified FN individuals using the Municipality of Registration field held at Manitoba Health. We compared the incidence and prevalence of MS between the FN and non-FN populations using negative binomial models. RESULTS: From 1984 to 2011, 5,738 persons had MS, of whom 64 (1.1%) were of FN ethnicity. The average annual incidence rate per 100,000 population was 8.15 (95% confidence interval [CI] 5.98-11.1) in the FN population and 15.7 (95% CI 15.1-16.3) in the non-FN population (incidence rate ratio 0.52; 95% CI 0.38-0.71). In 1984, the crude prevalence of MS per 100,000 population was 35.8 (95% CI 14.9-86.1) in the FN population and 113.3 (95% CI 106.3-120.8) in the non-FN population. Between 1984 and 2011, the age-standardized prevalence of MS increased by 351% to 188.5 (95% CI 146.6-230.4) in the FN population. In contrast, the prevalence of MS per 100,000 general population increased by 225%-418.4% (95% CI 405.8-431.0). CONCLUSIONS: The incidence and prevalence of MS are twofold lower in the FN population than the non-FN population. Nonetheless, the prevalence of MS in FN Manitobans is higher than in other indigenous populations outside Canada. Given reports of more rapid disability progression among FN Canadians with MS, and the rising prevalence of MS in this population, attention should be directed to the needs of this population.
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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 | 0.003 | 0.238 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".