Prevalence and Incidence of Rheumatoid Arthritis in Canadian First Nations and Non–First Nations People
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to determine the prevalence, incidence, and onset age at rheumatoid arthritis (RA) diagnosis in First Nations (FN) and non-FN populations in Manitoba, Canada. METHODS: Population-based administrative health records from April 1, 1995, to March 31, 2010, were accessed for all Manitobans. The FN population was identified using the Federal Indian Registry File. Crude and adjusted RA prevalence and incidence rates (adjusted for age, sex, health region of residence) were compared using Poisson regression and reported as relative rates (RRs) with 95% confidence intervals (CIs). Mean (CI) diagnosis age and physician visits were compared with Student t tests. RESULTS: Rheumatoid arthritis crude prevalence increased between 2000 and 2010 to 0.65%; adjusted RA prevalence in females was 1.0% and in males was 0.53%. The 2009/2010 adjusted RA prevalence was higher in FN than non-FN (RR, 2.55; CI, 2.08-3.12) particularly for ages 29 to 48 years (RR, 4.52; CI, 2.71-7.56). Between 2000 and 2010, crude RA incidence decreased from 46.7/100,000 to 13.4/100,000. Adjusted RA incidence remained higher in FN than non-FN (2000-2010 RR, 2.1; CI, 1.7-2.6; p < 0.0001) particularly for ages 29 to 48 years (RR, 4.6; CI, 2.8-7.4; p < 0.0001). The FN population was younger at diagnosis than the non-FN population (mean age, 39.6 years [CI, 38.3-40.8 years] vs. 53.3 years [CI, 52.7-53.9 years]; p < 0.0001). The FN population had more physician visits but fewer rheumatology visits than the non-FN population. CONCLUSIONS: Rheumatoid arthritis prevalence is increasing, and RA incidence is decreasing in Manitoba. The FN population has a greater prevalence and incidence of RA and is younger at diagnosis than the non-FN population. When combined with fewer rheumatology visits, this significant care gap highlights the need to optimize rheumatology care delivery to the FN 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".