Economic evaluation of a population-based osteoporosis intervention for outpatients with non-traumatic non-hip fractures: the “Catch a Break” 1i [type C] FLS
Bibliographic record
Abstract
Fracture liaison services (FLS) are advocated to improve osteoporosis treatment after fragility fracture, but there are few economic analyses of different models. A population-based 1i [=type C] FLS for non-hip fractures was implemented and it costs $44 per patient and it was very cost-effective ($9200 per QALY gained). Small operational changes would convert it from cost-effective to cost-saving. INTRODUCTION: After fragility fracture, <20% of patients receive osteoporosis treatment. FLS are recommended to address this deficit but there are very few economic analyses of different FLS models. Therefore, we conducted an economic analysis of a 1i (=type C) FLS called "Catch a Break (CaB)." METHODS: CaB is a population-based FLS in Alberta, Canada, that case-finds older outpatients with non-traumatic upper extremity, spine, pelvis, or "other" non-hip fractures and provides telephonic outreach and printed educational materials to patients and their physicians. Cost-effectiveness was assessed using Markov decision-analytic models. Costs were expressed in 2014 Canadian dollars and effectiveness based on model simulations of recurrent fractures and quality-adjusted life years (QALYs). Perspective was healthcare payer; horizon was lifetime; and costs and benefits were discounted 3%. RESULTS: Over 1 year, CaB enrolled 7323 outpatients (mean age 67 years, 75% female, 69% upper extremity) at average cost of $44 per patient. Compared with usual care, CaB increased rates of bisphosphonate treatment by 4.3 to 17.5% (p < 0.001). Over their lifetime, for every 10,000 patients enrolled in CaB, 4 hip fractures (14 fractures total) would be avoided and 12 QALYs gained. Compared with usual care, incremental cost-effectiveness of CaB was estimated at $9200 per QALY. CaB was cost-effective in 85% of 10,000 probabilistic simulations. Sensitivity analyses showed if "other" fractures were excluded and intervention costs reduced 25% that CaB would become cost-saving. CONCLUSIONS: A relatively inexpensive population-based 1i (=type C) FLS was implemented in Alberta and it was very cost-effective. If CaB excluded "other" fractures and decreased intervention costs by 25%, it would be cost-saving, as would any FLS that was more effective and less expensive.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".