The Direct Economic Burden of Gout in an Elderly Canadian Population
Bibliographic record
Abstract
OBJECTIVE: To estimate the direct healthcare cost and resource use from the public payer perspective between patients with incident gout and matched gout-free patients in Ontario. METHODS: Patients with incident gout aged ≥ 66 with uninterrupted Ontario Health Insurance Plan (OHIP) coverage in the 1-year baseline period were included in the study. Patients with gout were indexed at first gout diagnosis or prescription over the study period April 1, 2008, to March 31, 2014. Gout-free patients with no gout diagnosis within history were matched (up to 5:1) to each patient with gout. Linked medical records were analyzed until end of study, death, or OHIP ineligibility. Bang and Tsiatis adjusted healthcare costs and resource use were compared using bootstrap p-values and 95% CI. RESULTS: A total of 29,894 patients with gout and 148,231 gout-free patients were included in the study. Patients were 56% male, had a median Adjusted Clinical Group healthcare resource use band of moderate morbidity, and had a median age of 75-79 years. Baseline comorbidities were similar between groups except for renal disease. Analyzing 5-year total healthcare costs, patients with gout ($44,297) incurred a significantly higher average healthcare cost compared to gout-free patients ($33,965), for an incremental cost of $10,332 (95% CI $9617-$11,039; p < 0.01). Similar trends were observed in all individual healthcare component cost and use metrics. CONCLUSION: Following onset of gout, patients in Ontario incur significantly greater healthcare costs and resource use compared to matched gout-free patients. Alternative gout management strategies should be investigated to reduce the incremental burden of gout borne by the Ontario healthcare system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".