Direct costs of fractures in Canada and trends 1996–2006: A population-based cost-of-illness analysis
Bibliographic record
Abstract
Cost-of-illness (COI) analysis is used to evaluate the economic burden of illness in terms of health care resource (HCR) consumption. We used the Population Health Research Data Repository for Manitoba, Canada, to identify HCR costs associated with 33,887 fracture cases (22,953 women and 10,934 men) aged 50 years and older that occurred over a 10-year period (1996-2006) and 101,661 matched control individuals (68,859 women and 32,802 men). Costs (in 2006 Canadian dollars) were estimated for the year before and after fracture, and the change (incremental cost) was modeled using quantile regression analysis to adjust for baseline covariates and to study temporal trends. The greatest total incremental costs were associated with hip fractures (median $16,171 in women and $13,111 for men), followed by spine fractures ($8,345 in women and $6,267 in men). The lowest costs were associated with wrist fractures ($663 in women and $764 in men). Costs for all fracture types were greater in older individuals (p < 0.001). Similar results were obtained with regression-based adjustment for baseline factors. Some costs showed a slight increase over the 10 years. The largest temporal increase in women was for hip fracture ($13 per year, 95% CI $6-$21, p < 0.001) and in men was for humerus fracture ($11 per year, 95% CI $3-$19, p = 0.007). At the population level, hip fractures were responsible for the largest proportion of the costs after age 80, but the other fractures were more important prior to age 80. We found that there are large incremental health care costs associated with incident fractures in Canada. Identifying COI from HCR use offers a cost baseline for measuring the effects of evidence-based guidelines implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".