Determination of Rheumatoid Arthritis Incidence and Prevalence in Alberta Using Administrative Health Data
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to estimate the incidence and prevalence of rheumatoid arthritis (RA) in Alberta using administrative health data. METHODS: We identified RA cases in patients 16 years and older by applying a national case definition to linked administrative health data (ie, hospital discharge abstract records, physician claims, and health insurance registry records) using a unique personal identifier. Incidence and prevalence are reported for the 2015-2016 fiscal year and a trend analysis from 2011-2012 to 2015-2016. Incidence and prevalence estimates were standardized using the 2011 Canadian census population. RESULTS: In 2015-2016, the overall crude incidence was 0.74 [95% confidence interval (CI): 0.71-0.77] per 1000 and crude prevalence was 1.08% (95% CI: 1.07-1.09). The women-to-men crude incidence and prevalence sex ratios were 2.04 and 2.19, respectively. People aged 65 to 79 years had the highest incidence of RA, and the highest prevalence was observed among those 80 years and older. From 2011-2012 to 2015-2016, the overall age-standardized incidence decreased [0.97 (95% CI: 0.94-1.01) to 0.79 (95% CI: 0.76-0.82) per 1000], whereas age-standardized prevalence remained constant [1.17 (95% CI: 1.15-1.18) to 1.18 (95% CI: 1.17-1.19)]. CONCLUSION: In Alberta, there was a decreasing trend in RA incidence over the study period, whereas prevalence was stable. These estimates, combined with clinical data, will be used to measure system performance for quality improvement and to inform simulation modeling for planning the expected demand for health services for patients living with RA.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.001 | 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".