Trends in Gout and Rheumatoid Arthritis Hospitalizations in Canada From 2000 to 2011
Bibliographic record
Abstract
OBJECTIVE: Gout and rheumatoid arthritis (RA) are the 2 most common forms of inflammatory arthritis worldwide. As hospitalizations for both conditions lead to substantial health resource use, contemporary inpatient trends and associated costs may provide important benchmarks of disease burden. However, relevant data are limited. METHODS: We used PopulationData BC, a population-based administrative data set from Canada. We examined trends in the annual hospitalization and surgery rate of gout and RA from 2000 to 2011. Additionally, we examined annual trends in the inpatient cost burden of both conditions. We assessed annual trends in hospitalization and surgery rates using Poisson regression models and cost trends using linear regression models. RESULTS: From 2000 to 2011, the annual hospitalization rate for RA declined by 49% from 15.4 to 7.9 per 100,000 Canadian adults (P < 0.001), whereas that for gout doubled from 3.8 to 7.6 per 100,000 Canadian adults (P < 0.001). Approximately 31% of RA admissions were associated with hip or knee replacement surgery; the trend of these surgeries paralleled the declining trend in RA hospitalizations (P = 0.0097). The inpatient costs also reflected the hospitalization trends, with a 40% decrease in RA hospital costs, while gout costs more than doubled over the study period. CONCLUSION: Our findings indicate that hospitalization rates for gout have doubled over the past decade, while those for RA have decreased considerably. While these data provide an encouraging benchmark for RA care, they also highlight the critical need to improve gout management and prevention to mitigate its rising disease burden in Canada and beyond.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".