Incremental costs of fragility fractures: a population-based matched -cohort study from Ontario, Canada
Bibliographic record
Abstract
Using a matched cohort design, the 1-year excess cost of incident fragility fractures at any site was $26,341 per patient, with 43% of total excess costs attributed to hospitalization. The high economic burden of fractures in Ontario underscores the urgency of closing the secondary fracture prevention gap. INTRODUCTION: This retrospective real-world observational study was conducted to document the incremental costs associated with fragility fractures in Ontario, Canada. METHODS: Patients aged >65 years with an index fragility fracture occurring between January 2011 and March 2015 were identified from administrative databases and matched 1:1 to a cohort of similar patients without a fracture. Healthcare resource utilization data were extracted from healthcare records and associated costs were calculated on a per-patient level and for the province of Ontario. Costs were presented as 2017 Canadian dollars. RESULTS: The eligible cohort included 115,776 patients with a fragility fracture. Of these, 101,773 patients were successfully matched 1:1 to a non-fracture cohort. Overall, hip fractures (n = 31,613) were the most common, whereas femur fractures (n = 3002) were the least common type. Hospitalization and continuing care/home care/long-term care accounted for more than 60% of 1-year direct costs, whereas 5% was attributed to medication costs. First-year costs per patient in the fracture cohort were approximately threefold higher versus the non-fracture cohort (mean $37,362 versus $11,020, respectively). The incremental first-year direct healthcare costs of fragility fractures for the province of Ontario were calculated at $724 million per year. CONCLUSIONS: Fragility fractures were associated with a threefold increase in overall mean healthcare costs per patient compared to patients without fractures. With an aging population, there is an urgent need for improved prevention strategies for patients at high-risk of fracture to decrease the economic burden of fragility fractures on the Canadian 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".