Distribution and Drivers of Average Direct Cost of Osteoarthritis in Canada From 2003 to 2010
Bibliographic record
Abstract
OBJECTIVE: To estimate the distribution and drivers of the average direct cost of osteoarthritis (OA) in Canada using a population-based health microsimulation model of OA from 2003 to 2010. METHODS: We used a previously published microsimulation model to estimate the distribution of average cost of OA across different cost components and OA stages. OA stages were defined according to the patient flow within the health care system. Cost components associated with pharmacologic and nonpharmacologic treatments, physician visits, and hospitalization were included. Scenario analysis was performed to evaluate average cost drivers from 2003 to 2010. RESULTS: During the study period, the OA population size grew from 2.9 to 3.6 million, while the average cost increased from $577 to $811 (Canadian) per patient per year. The highest increase in share of cost components was for total joint replacement (TJR) surgery (24% to 32%). The highest average cost was incurred by patients in stage 4 (during and after revision surgery), while around 80% of OA patients were in stage 1 (OA diagnosed but has not visited an orthopedic surgeon). Increase in the proportion of OA patients receiving TJR surgeries (34%) and price inflation (29%) were the most significant drivers of average cost. CONCLUSION: The average cost of OA has been increasing during the study period mostly due to an increase in the proportion of patients receiving TJR surgeries and price inflation. The distribution of average cost of OA across disease stages needs to be considered when designing policies targeting specific aspects of OA care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".