Cost-Effectiveness of Two Inexpensive Postfracture Osteoporosis Interventions: Results of a Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Most older patients are not treated for osteoporosis after fragility fracture. In a 3-armed randomized trial, we reported that 2 inexpensive mail-based interventions, one directed at physicians and the other at physicians plus patients, increased 1-year osteoporosis treatment starts by 4% and 6% (respectively) compared with usual care starts of 11%. The cost-effectiveness of these interventions is unknown. METHODS: The incremental cost-effectiveness of interventions compared with usual care was assessed using Markov decision-analytic models. Costs were expressed in 2010 Canadian dollars and long-term effectiveness based on quality-adjusted life years (QALYs) gained derived from hypothetical model simulations. The perspective was third-party health care payer; the time horizon was lifetime; and the costs and benefits were discounted 3%. RESULTS: The physician intervention cost was $7.12 per patient, whereas the physician plus patient intervention cost was $8.45. Compared with usual care, the economic simulation demonstrated that for every 1000 patients getting the physician intervention, there were 2 fewer fractures, 2 more QALYs gained, and $22,000 saved. Compared with physician intervention, the simulation demonstrated that for every 1000 patients receiving physician plus patient intervention, there was 1 fewer fracture and 1 more QALY gained, with $18,000 saved. Both interventions dominated usual care and were cost saving or highly cost effective in 67% of 10 000 probabilistic simulations. Although the physician plus patient intervention cost was $1.33 more per patient than the physician intervention, it was still the most economically attractive option. CONCLUSIONS: Pragmatic mail-based interventions directed at patients with recent fractures and their physicians are a highly cost-effective means to improving osteoporosis management and both interventions dominated usual 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".