Population-based epidemiologic studies of gout in British Columbia, Canada
Bibliographic record
Abstract
Introduction: This thesis contains original analyses aimed at better understanding the burden of gout, an excruciatingly painful form of inflammatory arthritis, in the Canadian context. While gout is increasingly recognized as the most common form of inflammatory arthritis worldwide (e.g., reported prevalence of 3.9% and 2.5% in the United States and United Kingdom, respectively), no Canadian trend data are available. Objectives: 1) To evaluate the contemporary prevalence and incidence of gout over the past decade, as well as gout treatment patterns and comorbidity burden. 2) To evaluate the burden of hospitalized gout and corresponding inpatient costs as compared to rheumatoid arthritis (RA), another inflammatory joint disease known to incur substantial resource use. Methods: To address both objectives, I used PopulationData BC, a large administrative database spanning the province of British Columbia (BC). For Objective 1, I used physician and hospital visits to identify gout cases and estimate the annual trends in prevalence and incidence among the general population. I additionally used data from PharmaNet, BC’s prescription drug database, to examine gout treatment patterns (i.e., urate-lowering therapy, colchicine, glucocorticoids, and non-steroidal anti-inflammatory drugs) over the same time period. For Objective 2, I used hospital diagnoses and procedure codes to assess annual trends in hospitalizations and joint surgeries as well as inpatient costs for both gout and RA. Results: 1) Both the prevalence and incidence of gout have increased over the past decade (i.e., a 59% and 48% increase, respectively), while the prescription of gout treatment remains low. 2) The hospitalization rates for gout have doubled over the past decade, while those for RA have declined by 49%. 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: Altogether, this thesis provides evidence that the burden of gout in Canada is substantial and increasing. These findings are further contrasted against the hospitalization burden of RA, which has decreased considerably over the same period. This thesis highlights the critical need to improve gout prevention and care 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".